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Сборник аннотаций

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Секция 1. Машинное обучение в фундаментальной физике

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32. Machine Learning in Gamma Astronomy

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34. Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning

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57. Методы машинного обучения в задаче предсказания дифференциальных сечений и структурных функций электророждения пиона на протоне в резонансной области.

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43. Gamma/hadron separation in the TAIGA experiment with neural network methods

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57. Boosting Novelty Detection Neural Networks with Rational Activations

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67. Кодирование входных сигналов в терминах ориентированных подграфорв в спайковой нейронной сети с локальной пластичностью.

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46. Определение направления ШАЛ по данным TAIGA HiSCORE с помощью полносвязных нейросетей

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41. Морфологическая классификация джетов активных ядер галактик

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42. Application of Kolmogorov-Arnold Networks in high energy physics

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44. Генерация показаний наемных детекторов и поиск аномалий в данных с помощью нейронных сетей

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хх. Нейросетевое моделирование оптических солитонов, описываемых обобщенными нелинейными уравнениями Шредингера

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25. Реконструкция ШАЛ, зарегистрированных флуоресцентными телескопами, с помощью нейронных сетей

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46. Определение направления ШАЛ по данным TAIGA HiSCORE с помощью полносвязных нейросетей

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45. Применение нейронных сетей для вычисления континуальных интегралов в квантовой теории

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40. Исследование механизма генерации гамма-вспышек в блазарах

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70. Многомерная оптимизация детекторных систем на примере мюонной защиты в эксперименте SHiP

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39. Решение задач математической физики на сетях радиальных базисных функций

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58. Enhanced Image Clustering with Random-Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature Estimation

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Секция 2. Машинное обучение в науках об окружающей среде

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9.Сравнительный анализ методов машинного и глубокого обучения в задаче классификации волновых форм полного электронного содержания

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72. Application of machine learning methods for analyzing data from semiconductor gas sensors in dynamic temperature mode.

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64. ПРОГНОЗИРОВАНИЕ ХАРАКТЕРИСТИК ВОЗВРАТНОЙ МИГРАЦИИ НЕРКИ В УСТЬЕ РЕКИ ФРЕЙЗЕР С ПРИМЕНЕНИЕМ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ

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56. Обзор поточечных и комплексных мер качества в исследованиях атмосферы и океана

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36. Статистическое прогнозирование уровня загрязнения воздуха частицами PM10 и PM2.5 в городских агломерациях в условиях сложного рельефа с применением методов машинного обучения на примере г. Гренобль

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49. Аппроксимация пространственно-временной изменчивости городского острова тепла Москвы методами машинного обучения

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3. Опыт использования методов машинного обучения в задачах диагностирования геоиндуцированных токов в высокоширотных энергосистемах

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72. Application of machine learning methods for analyzing data from semiconductor gas sensors in dynamic temperature mode

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49. Машинное обучение и глубокое обучение в науках об атмосфере, океане и климате: достижения и перспективы в 2024 году

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71.Towards the Statistical Correction of High-Resolution Weather Forecasts from the WRF Model employing Deep Learning

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74. The Ob-Yenisei plume movement analysis in the Kara Sea using artificial neural networks

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72. Прогнозирование состояния магнитосферы Земли с помощью специального алгоритма для работы с многомерными временными рядами

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36. Методы вероятностного программирования при реконструкции событий многоканального изображающего детектора: ЭЛЬФЫ и ТРЕКИ

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71. Применение методов машинного обучения для идентификации полярных мезомасштабных циклонов в данных численного моделирования атмосферы

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Секция 3. Машинное обучение в естественных науках

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47. Предсказание структуры дефектов MoS2 по заданным свойствам

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61. Estimation of signals in white noise using neural networks modeling

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37. Архитектура "трансформер" для анализа рисков группового влияния нутриентов пищи

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68. Метод сегментации очагов опухолевого поражения глаза ретинобластомой на основе глубокого обучения

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73. Nonlinear relevance estimation of multicollinear features for reducing the input dimensionality of optical spectroscopy inverse problem

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69. Use of Neural Network Approximation of the Parameters-Property Relationship in Synthesis of Carbon Dots

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62. Improving Representativity of Spectroscopic Data using Variational Autoencoders: Approaches and Problems

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68. Астрофизика частиц и анализ экспериментальных данных

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65. Comparative Analysis of the Procedures to Forecast the Kp Geomagnetic Index by Machine Learning

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50. Сети глубокого обучения для построения виртуальных датчиков технологических процессов нефтепереработки

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37. Нейронные операторы для гидродинамического моделирования подземных хранилищ газа (ПХГ)

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46. Модель машинного обучения на гауссовских процессах для предсказания энерговыделения в топливных ячейках активной зоны исследовательского ядерного реактора

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47. An effective algorithm for predicting human fatigue using a portable Brain-Computer Interface

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55. Алгоритм классификации премоторных потенциалов по сигналу электроэнцефалограммы для нейрореабилитации с помощью интерфейса «мозг-компьютер» замкнутого цикла

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75. Spiking neural network actor-critic reinforcement learning with temporal coding and reward modulated plasticity

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48. Сверточные нейронные сети для создания углеродного фотолюминесцентного наносенсора ионов металлов

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73. Kolmogorov-Arnold Networks vs Multi-Layer Perceptron: Solution of an Inverse Problem of Exploration Geophysics

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67. Модификация визуального трансформера методом b-cos для повышения интерпретируемости в задаче классификации изображений дерматоскопии

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73. Machine Learning for NICA SPD Aerogel Reconstruction

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52. Improving Physics-Informed Neural Networks via Quasi-classical Loss Functionals

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42. Применение свёрточных нейронных сетей для выделения событий ШАЛ в эксперименте СФЕРА-3

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40. Дескрипторы для определения локальной структуры атомистических моделей аморфных льдов

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71. Deep learning-based eye anatomical structures synthetic images generating: a comparative analysis of techniques and models

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47. Engineering Point Defects in Transition Metal Dichalcogenides for Tailored Material Properties

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51. Машинное обучение и 3D анализ сцен в оценке сложноизмеряемых геометрических характеристик тела человека для задач биомедицины

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